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Many companies are eager to integrate AI into their workflows, but face a common challenge: traditional AI systems lack access to proprietary, up-to-date business information. Retrieval-Augmented Generation (RAG) addresses this by enabling AI to retrieve relevant internal data before generating responses, ensuring outputs are both accurate and context-specific. RAG operates by first retrieving pertinent information from a company's documents, databases, or internal sources, and then using this data to generate informed answers. This approach allows AI systems to provide precise responses based on proprietary data, even if that information wasn't part of the model's original training.

Artificial intelligence (AI) agents help businesses by completing tasks independently, without needing constant instructions from people. Unlike simple AI tools or regular automation, AI agents can think through steps, make their own decisions, fix mistakes, and adapt if things change. They use different tools to find information, take actions, or even coordinate with other agents to get complex work done. Because AI agents can handle tasks on their own, they can be useful in areas like customer support, sales, marketing, and even writing software. Platforms that don't require coding make it easier for more people to create and use these agents. Businesses that understand how AI agents differ from simpler AI tools can better plan how to use them effectively, making their operations smoother and more efficient.

AI is changing how work gets done. Instead of replacing jobs, it helps with everyday tasks. Companies are looking for people who can work across different areas and use AI tools well. Entry-level roles are becoming more about checking AI’s work than doing it from scratch. The key is knowing how to ask the right questions and starting small with AI.

Heyra uses AI across three key stages of software development: from early ideas to structured product requirements, from product requirements to working prototypes, and from prototypes to production-ready code. Tools like Lovable, Cursor, and Perplexity allow both technical and non-technical team members to contribute earlier and move faster. This speeds up development, improves collaboration, and reshapes team workflows.